Recent efforts applied deep learning in the context of IoT.
In order to improve the predictive accuracy of IoT applications, researchers employed deep learning to model complicated sensing tasks [1]}, [2]}. In order to improve system efficiency at executing neural networks on low-end IoT devices, efforts have been made to compress model parameters and/or structures in a manner that does not entail (almost any) accuracy loss [3]}, [4]}, [5]}, [6]}.
Recent work in the context of IoT also addressed mathematical foundations for quantifying confidence in deep learning predictions to support mission-critical applications. The work produced deep neural networks that offer well-calibrated uncertainty estimates in results [7]}, [8]}, [9]}, [10]}.
Finally, the challenge of insufficient labeling of IoT data was addressed by introducing semi-supervised approaches for deep learning that allow neural network training using mostly unlabeled data [11]}.
However, none of the aforementioned IoT-inspired efforts addressed the customization of learning machinery to a different signal space inspired by the physics of measured processes; namely, the frequency domain.
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